CSQ126R235. This role is hybrid with 3 days in our Plano office.
Are you passionate about solving challenging technical problems, working with cutting-edge big data technologies, and growing your expertise in Apache Spark™, cloud platforms, and data engineering? Join our global team and make an impact by helping customers achieve their goals with Databricks!
We are looking for a Staff-level Spark Technical Solutions Engineer with a strong data engineering background and hands-on Spark experience. In this role, you’ll work closely with our customers to solve complex technical challenges related to Spark, machine learning, Delta Lake, streaming, and our Lakehouse platform. You’ll use your technical expertise and communication skills to guide customers in their Databricks journey, ensuring they maximize the value of our platform.
The Impact You Will Have- Analyze and troubleshoot Spark issues, such as job performance and slowness, using tools like Spark UI, DAG, and event logs.
- Solve problems related to Spark core, Spark SQL, structured streaming, Delta, and other Databricks runtime features.
- Help customers optimize Spark performance in areas like memory management, streaming, and data integration.
- Work directly with strategic customers to resolve day-to-day Spark and cloud-related issues.
- Collaborate with Account Executives, Customer Success Engineers, and Solution Architects to address customer needs.
- Collaborate with the R&D team to identify and escalate complex technical challenges, driving in-house supportability solutions within the Lakehouse platform.
- Provide live support via screen-sharing sessions, Slack, and meetings to resolve major Spark issues.
- Create and maintain technical documentation, including knowledge base articles and manuals.
- Coordinate with engineering teams to report and track product defects.
- Participate in on-call rotations for handling escalations and incidents.
- Offer best practices for Spark performance and custom-built solutions.
- Advocate for customers and their success.
- Contribute to the development of internal tools and automation.
- Support integrations between Databricks and third-party platforms.
- Track and manage support tickets to meet SLAs.
- Continuously learn and improve your expertise in Databricks, AWS, and Azure.
- 8-12 years of experience developing Python, Java, or Scala applications in data engineering or consulting roles.
- 3+ years of hands-on experience with Spark (required) and other big data technologies like Hadoop, Kafka, or machine learning at a production scale.
- Proven experience troubleshooting and optimizing Hive and Spark applications.
- Knowledge of JVM, memory management, and garbage collection is a plus.
- Familiarity with SQL databases and ETL tools (e.g., Informatica, Oracle, Teradata) is preferred.
- Hands-on experience with AWS, Azure, or GCP is a plus.
- Excellent written and verbal communication skills.
- Basic Linux/Unix skills are a bonus.
- Knowledge of data lakes and slowly changing dimensions (SCD) is a plus.
- Strong problem-solving and analytical skills, especially in distributed big data environments.
Pay Range Transparency
Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.
About Databricks
Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.
Benefits
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region, please visit https://www.mybenefitsnow.com/databricks.
Our Commitment to Diversity and Inclusion
At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.
Compliance
If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.
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What We Do
As the leader in Unified Data Analytics, Databricks helps organizations make all their data ready for analytics, empower data science and data-driven decisions across the organization, and rapidly adopt machine learning to outpace the competition. By providing data teams with the ability to process massive amounts of data in the Cloud and power AI with that data, Databricks helps organizations innovate faster and tackle challenges like treating chronic disease through faster drug discovery, improving energy efficiency, and protecting financial markets.
